CN113777631A - CBOC signal fuzzy-free capture algorithm based on sub-correlation function under sinusoidal frequency modulation interference - Google Patents
CBOC signal fuzzy-free capture algorithm based on sub-correlation function under sinusoidal frequency modulation interference Download PDFInfo
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- CN113777631A CN113777631A CN202010521067.5A CN202010521067A CN113777631A CN 113777631 A CN113777631 A CN 113777631A CN 202010521067 A CN202010521067 A CN 202010521067A CN 113777631 A CN113777631 A CN 113777631A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
- G01S19/30—Acquisition or tracking or demodulation of signals transmitted by the system code related
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
- G01S19/10—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing dedicated supplementary positioning signals
- G01S19/12—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing dedicated supplementary positioning signals wherein the cooperating elements are telecommunication base stations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/24—Acquisition or tracking or demodulation of signals transmitted by the system
- G01S19/29—Acquisition or tracking or demodulation of signals transmitted by the system carrier including Doppler, related
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Abstract
The invention requests to protect a fuzzy capture-free algorithm for a CBOC signal under sinusoidal frequency modulation interference, and belongs to the field of signal processing. And (3) performing sinusoidal frequency modulation conversion on the received CBOC signal containing the sinusoidal frequency modulation interference, and determining the position of the sinusoidal frequency modulation interference through the characteristic that the sinusoidal frequency modulation interference is gathered in a DSFMT domain and performing interference zero setting processing. Then dividing the subcarrier modulated by the pseudo code into 12 groups, correlating the received signal after interference suppression processing with the 12 groups of subcarrier to obtain 12 groups of sub-correlation functions, and finally synthesizing an accurate unambiguous acquisition algorithm according to the corresponding relation among the 12 groups of sub-correlation functions. Experiments show that the method can effectively inhibit sinusoidal frequency modulation interference and can completely eliminate secondary peaks, and meanwhile, the larger the grouping number of the secondary carriers is, the narrower the width of the main peak is, and the better the performance is. Moreover, the method has better performance than the traditional ASPECT algorithm and SCPC algorithm, and has important significance for capturing signals in practical application.
Description
Technical Field
The invention belongs to the relevant field of CBOC signal capture in navigation communication, and particularly relates to a fuzzy-free capture algorithm of a CBOC signal based on a sub-correlation function under sinusoidal frequency modulation interference.
Background
In recent years, Global Navigation Satellite Systems (GNSS) have been widely used in military and civil fields, and thus more and more satellite navigation systems have been developed, such as Global Positioning System (GPS) managed by the united states government, Galileo satellite navigation system (Galileo) managed by the european union and european space agency, global satellite navigation system (GLONASS) managed by the russian federal government, BeiDou satellite positioning communication system (BDS) managed by the national government, regional navigation positioning system (IRNSS) managed by the indian government, and the like. This makes the navigation band increasingly crowded. Under such a background, BOC and its derived signals are widely used in a new generation of navigation satellite systems because of their spectrum splitting characteristics, which can realize compatibility of new and old services. However, in practical applications, the received BOC signal is often subject to various interferences due to the far propagation path and the low received power. Although the CBOC signal is derived from the BOC signal, although it has stronger interference rejection, its spectrum still generates distortion under strong interference, so that it cannot be accurately captured. Therefore, the method has important significance and value in researching the receiving performance of the BOC and the derivative signals thereof in various interference environments and providing an effective and feasible anti-interference capturing method.
Aiming at the problem that CBOC signal frequency spectrum is seriously distorted under sinusoidal frequency modulation interference and cannot be captured, a non-fuzzy capture algorithm combining sinusoidal frequency modulation transformation with a sub-correlation function is provided, the method can realize the non-fuzzy capture of the CBOC signal under the sinusoidal frequency modulation interference, and a BOC capture algorithm combining a frequency domain FFT lapped transform narrowband interference suppression method and a correlation reconstruction method is provided in the literature 'BOC signal capture under a narrowband interference environment'. The algorithm firstly reduces the frequency spectrum leakage of signals and the loss of the signals by carrying out overlapping windowing on the signals, then effectively inhibits interference signals by a frequency domain notch technology, and finally captures the processed BOC signals by using a relevant reconstruction method. The literature 'research on anti-chirp interference technology of GNSS receiver based on fractional Fourier transform' adopts FRFT to inhibit broadband chirp interference, and then adopts FRFT to capture subsequent signals. However, the two documents mainly aim at narrowband interference and broadband interference, few researches aim at sinusoidal frequency modulation interference, and for a non-fuzzy acquisition algorithm, the most common is a PCF analysis method, and the main idea of the PCF method is to generate a unimodal correlation function with the same main peak width as the main peak width of the autocorrelation function of the BOC signal by introducing two specially designed local codes, so that the characteristic of the narrow main peak of the BOC signal is retained. For example, the document "BOC and its derived signal general unambiguous acquisition analysis" proposes an improved method of unambiguous acquisition based on Pseudo Correlation Function (PCF). Firstly, constructing a uniform expression of a cross-correlation function of a BOC signal according to the concept of a shape code vector, and providing two groups of new shape code vectors; then, a single peak is obtained by cross-correlation synthesis of the received signal and a reference signal corresponding to the shape code vector; finally, the non-fuzzy capture method is obtained by synthesizing a unimodal and autocorrelation function. However, the algorithm can only capture BOC and its derived signals in an interference-free environment, and further verification is needed to determine whether the BOC and its derived signals can be captured under sinusoidal FM interference.
Disclosure of Invention
The technical problem to be solved by the invention is that the frequency spectrum of the received signal of the receiver is completely distorted under the sinusoidal frequency modulation interference, so that the traditional algorithm cannot capture the received signal. For the sinusoidal frequency modulation interference, sinusoidal frequency modulation transformation can be used for carrying out interference suppression on the sinusoidal frequency modulation interference, for the case of a capture algorithm, a capture algorithm based on a correlation function formed by sub-correlation functions can be used, and aiming at the problems, a non-fuzzy capture algorithm based on the combination of the sinusoidal frequency modulation transformation and the sub-correlation functions is provided.
The technical scheme for solving the technical problems is as follows: under the condition of sinusoidal frequency modulation interference, based on a CBOC signal unambiguous acquisition algorithm combining sinusoidal frequency modulation transformation and a sub-correlation function, the steps are that firstly, the suppression of sinusoidal frequency modulation interference is realized through sinusoidal frequency modulation transformation, because the sinusoidal frequency modulation interference signals can gather in the DSFMT domain, the interference position can be judged and set to zero through the characteristic so as to realize the suppression of sinusoidal frequency modulation interference, then dividing the subcarrier modulated by the pseudo code into 12 groups, correlating the received signal after interference suppression processing with the 12 groups of subcarrier to obtain 12 groups of sub-correlation functions, synthesizing the correlation function only with the main peak according to a certain algebraic relational expression according to the corresponding relation among the 12 groups of sub-correlation functions, finally comparing the maximum value of the correlation function with a preset threshold, if the maximum value exceeds the threshold, indicating that the capturing is successful, and if the maximum value does not exceed the threshold, adjusting the combined spread spectrum code to continue the operation. It should be noted that the grouped number is an integer multiple of 12, and the larger the grouped number is, the narrower the main peak is, and the better the performance is.
The invention fuses sinusoidal frequency modulation transformation and sub-correlation functions, is applied to the capture process of the CBOC signal under sinusoidal frequency modulation interference, introduces a model of the CBOC signal in detail, deduces the principle that the sub-correlation functions synthesize the correlation functions to complete the unambiguous capture of the CBOC signal, and realizes the specific steps of the method, verifies the effectiveness of the sinusoidal frequency modulation transformation in inhibiting the sinusoidal frequency modulation interference, overcomes the defect that the traditional capture method can not complete the capture of the CBOC signal under the sinusoidal frequency modulation interference, and has great significance in the actual capture of the CBOC signal.
Drawings
FIG. 1 is a schematic diagram of unambiguous acquisition of a CBOC signal under sinusoidal FM interference in accordance with the present invention;
FIG. 2 DSFMT domain spectrum of CBOC signal containing sinusoidal FM interference according to the present invention
FIG. 3 is an exploded view of the CBOC (6,1,1/11) signal subcarrier of the present invention
FIG. 4 sub-correlation function of CBOC (6,1,1/11) of the present invention
FIG. 5 CBOC (6,1,1/11) combined correlation function of the present invention
FIG. 6 pre-and post-CBOC (6,1,1/11) interference suppression spectral comparison of the present invention
FIG. 7 different algorithm normalized amplitude output comparison of the present invention
FIG. 8 normalized amplitude output comparison under different packet numbers for the present invention
FIG. 9 comparison of the main peak ratio means of different algorithms of the present invention
FIG. 10 comparison of the ratio of the mean to the ratio of the main peaks for different numbers of packets according to the present invention
Detailed Description
The invention is further described with reference to the following drawings and specific examples.
The method comprises the following steps: fig. 1 is a specific block diagram showing the implementation of the unambiguous acquisition algorithm of CBOC signals under sinusoidal frequency modulation interference according to the present invention, and the specific steps are as follows: firstly, sinusoidal frequency modulation conversion is carried out on received signals, the position of sinusoidal frequency modulation interference is determined through the characteristic that sinusoidal frequency modulation interference signals are in a gathering state under the sinusoidal frequency modulation conversion, zero setting processing is carried out, accordingly, suppression of the sinusoidal frequency modulation interference is achieved, then a carrier signal is oscillated by an oscillator, CBOC (6,1,1/11) modulation signals are demodulated, and FFT conversion processing is carried out on the demodulated CBOC (6,1,1/11) signals. And carrying out secondary subcarrier modulation on the local pseudo code sequence. Selecting chips from the modulated signal, cutting N different chips, performing FFT conjugate operation, multiplying the conjugated sub-signal with the received CBOC (6,1,1/11) signal after FFT conversion, and taking IFFT to obtain each sub-correlation function of the CBOC (6,1,1/11) signal autocorrelation function. The resulting sub-correlation functions are combined into a correlation function according to the capture algorithm mentioned herein. And comparing the maximum value of the correlation function with a threshold, entering a tracking stage if the maximum value is greater than the set threshold, and repeating the steps if the maximum value is less than the threshold.
Step two: fig. 2 shows the spectral line of the CBOC signal with sinusoidal frequency modulation interference in the DSFMT domain, and as can be seen from fig. 2, DSFMT is performed on the CBOC signal with sinusoidal frequency modulation interference, the CBOC signal is gathered in the DSFMT domain, and through this point, the position of the interference spectral line can be obtained, and interference zero setting processing is performed, and then IDSFMT is performed to obtain the CBOC signal.
Step three: CBOC (6,1,1/11) signal subcarrier decomposition process analysis. Pseudo code modulation of CBOC (6,1,1/11) signalsThe subcarriers of the system are represented as
Dividing the data into 12 parts, and recording a certain part corresponding to one subcarrier as
Wherein l ∈ {0,1,2, … 11}, and the specific decomposition process is shown in fig. 3. Therefore, the subcarriers can be further expressed as:
step four: the sub-correlation function of the CBOC (6,1,1/11) signal forms the analysis. The normalized autocorrelation function of the CBOC (6,1,1/11) signal can be expressed as:
wherein λ isl(τ) represents a sub-correlation function of some portion of the CBOC (6,1,1/11) signal. There are 12 CBOC (6,1,1/11) signal sub-correlation functions, the first 6 shown in fig. 4(a) and the last 6 shown in fig. 4 (b).
And 5: and synthesizing a fuzzy-free correlation function process analysis by using the sub-correlation functions. As can be seen from fig. 4(a) and 4(b), the 12 sub-correlation functions are composed of sub-carriers with different phases, the correlation value of each sub-carrier has positive or negative, and there is a symmetric relationship, that is:
Rl(τ)＝RN-1-l(τ) (3)
an algebraic idea is obtained through a Pseudo Correlation Function (PCF),
the relation can be found:
the sub-correlation functions of the CBOC (6,1,1/11) signals are combined as in equation (5) to obtain the graph shown in fig. 5.
According to fig. 5, the combined correlation functions are all positive, and the main peak of the last graph is narrowest, from which the final correlation function can be obtained as:
step six: fig. 6 is a comparison of the frequency spectra before and after CBOC (6,1,1/11) interference suppression. As can be seen from fig. 6, the spectrum of the CBOC (6,1,1/11) signal under the SFM interference is severely distorted, which may cause the CBOC (6,1,1/11) signal to be captured inaccurately, and the spectrum of the CBOC (6,1,1/11) after the SFM interference suppression is not much different from the spectrum of the original CBOC (6,1,1/11), which shows that the algorithm of the present invention can well suppress the SFM interference in the CBOC (6,1,1/11) signal to provide a relatively good environment for the capture of the following CBOC (6,1, 1/11).
Fig. 7 is a comparison of normalized amplitude outputs for different algorithms. It can be seen from fig. 7 that the autocorrelation function of the CBOC (6,1,1/11) signal has the characteristic of multiple peaks, the secondary peak can cause ambiguity of capture, the ambiguity of the autocorrelation function can be basically eliminated through the ASPeCT algorithm and the SCPC algorithm, but the secondary peak is not sufficiently eliminated, and the false capture can be caused to a certain extent, while the algorithm of the present invention not only completely eliminates the secondary peak, but also the width of the primary peak is narrower than the widths of the autocorrelation function, the ASPeCT algorithm and the SCPC algorithm, which indicates that the performance of the algorithm of the present invention is optimal among the four algorithms.
Fig. 8 is a graph of normalized amplitudes of different groups of outputs of subcarriers modulated by pseudo-code. As can be seen from fig. 8, the algorithm of the present invention completely eliminates the secondary peak, and the width of the main peak is narrower as the number of the sub-carrier components increases. Such as: the main peak width for subcarrier grouping into 24 groups is one half of the main peak width for subcarrier grouping into 12 groups, the main peak width for subcarrier grouping into 48 groups is one half of the main peak width for subcarrier grouping into 24 groups, and the main peak width for subcarrier grouping into 36 groups is between the main peak widths for subcarrier grouping into 24 groups and subcarrier grouping into 48 groups.
Fig. 9 is a comparison of the main peak ratio mean values under different algorithms, and it can be seen from fig. 9 that the main peak ratio mean values before interference suppression are substantially all 0, because the frequency spectrum of the CBOC (6,1,1/11) signal under sinusoidal frequency modulation interference is severely distorted, which results in that the conventional non-fuzzy acquisition algorithm cannot acquire the signal, and for the main peak ratio mean value of the algorithm after interference suppression, which is greater than the ASPECT algorithm and the SCPC algorithm, this shows that the performance of the algorithm of the present invention is superior to the ASPECT algorithm and the SCPC algorithm.
Fig. 10 is a comparison of the main peak ratio mean values when the number of groups is different, and it can be seen from fig. 10 that, as the number of sub-carriers divided by the sub-carriers modulated by the pseudo code is larger, the larger the main peak ratio mean value is, the better the performance of the algorithm is, but as the number of sub-carrier groups is increased, the computational complexity is also increased, so that in practical application, the requirements of the system can be considered first, and then how many groups the sub-carriers need to be divided into can be determined.
Claims (4)
1. A fuzzy-free capture algorithm for CBOC signals under sinusoidal frequency modulation interference comprises the steps of performing sinusoidal frequency modulation transformation on received CBOC signals containing sinusoidal frequency modulation interference, determining the position of sinusoidal frequency modulation interference through the characteristic that the sinusoidal frequency modulation interference is gathered in a DSFMT domain, and performing interference zero setting processing. Then dividing the sub-carriers modulated by pseudo code into 12 groups, correlating the received signal after interference suppression processing with the 12 groups of sub-carriers to obtain 12 groups of sub-correlation functions, and then, according to the 12 groups of sub-correlation functions
This algebraic relation is combined, wherein,
it should be noted that the number of grouped groups only needs to satisfy an integer multiple of 12, and the more the number of grouped groups, the narrower the main peak thereof and the better the performance.
2. The acquisition method according to claim 1, characterized in that the CBOC (6,1,1/11) received signal under sinusoidal chirp interference has such characteristics.
The CBOC signal is represented as:
wherein S (t) is a baseband direct spread signal generated by modulating a pseudo code sequence and an information sequence, Sc (t) represents a CBOC subcarrier, f0Which is representative of the primary carrier frequency,
indicating the primary carrier initial phase. The subcarriers of the CBOC signal are represented as: sc (t) ═ w1BOC(1,1)(t)±w2BOC (6,1) (t). Wherein, w1，w2Needs to satisfy w1+w21, the CBOC modulates the data signal and the navigation signal simultaneously. Therefore, the CBOC modulation signal with SFM interference and white gaussian noise is received as r (t) sCBOC(t)+xSFM(t)+n0(t) in the formula, n0(t) is additive white Gaussian noise with mean 0 and variance σ2；xSFM(t) is an SFM interference signal; expressed as: x is the number ofSFM(t)＝A′exp{j[2πfct+mfsin(2πfmt)]-wherein a' is the SFM interference signal amplitude; f. ofcIs the carrier frequency; f. ofmIs the modulation frequency; m isfIs the modulation factor.
3. The estimation method according to claim 1, the sinusoidal chirp transformation characteristic of the sinusoidal chirp interference signal is:
when f ism＝fm0And l ≠ l0The result of DSFMT is a function of Δ l/fm0Is a Bessel function of a variable when fm≠fm0And l ≠ l0The DSFMT results are then one in l/fmIs a Bessel function of a variable when fm≠fm0And l ═ l0When the DSFMT results in a number of l0/fmBeing Bessel functions of variables, their amplitudes being all
And (4) weighting. But when fm＝fm0And l ═ l0The DSFMT distribution of the signal takes a maximum value. Therefore, the DSFMT is carried out on the SFM interference, the SFM interference can gather in the DSFMT domain, and the interference position can be judged through the characteristic to carry out zero setting processing on the interference position, so that the suppression of the SFM interference is completed.
4. The unambiguous acquisition method according to claim 1, wherein the ambiguity of acquisition of the BOC signal due to the secondary peak of the autocorrelation function is solved well by the sub-correlation function method, and with respect to the sub-correlation function method, the sub-carriers modulated by the pseudo code are divided into 12 groups, and then correlated with the interference-suppressed received signal to obtain 12 groups of sub-correlation functions, and then the sub-correlation functions are combined according to an algebraic relational expression to obtain the unambiguous correlation function.
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| CN101881834A (en) * | 2010-06-24 | 2010-11-10 | 上海交通大学 | System and method for receiving CBOC (Community-Based Outpatient Clinic) modulating signal |
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| CN101881834A (en) * | 2010-06-24 | 2010-11-10 | 上海交通大学 | System and method for receiving CBOC (Community-Based Outpatient Clinic) modulating signal |
| CN108196274A (en) * | 2018-01-24 | 2018-06-22 | 桂林电子科技大学 | Be applicable in BOC (n, n) signal without fuzziness catching method and device |
| CN109375242A (en) * | 2018-10-30 | 2019-02-22 | 重庆邮电大学 | BOC signal capture algorithm under high dynamic based on PMF-FRFT |
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